Many species and larger taxonomic groups, especially invertebrates, have been little studied in terms of their patterns of geographical distribution - biogeography - and even basic information, inventories and assessments are missing. A key reason for this is that collecting and sampling has been too limited and too uneven: there are simply no good baseline data on distributions.

Ian Kitching of the NHM Life Sciences Department, with colleagues from the University of Basel, Switzerland, and Yale University, USA, set out to establish why inventories for the hawkmoths of Sub-Saharan Africa are incomplete, considering human geographical and associated environmental factors.

Xanthopan morganii praedicta - a hawkmoth found in Madagascar and East Africa

They used a database of hawkmoth distribution records to estimate species richness across 200 x 200 km map grid cells and then used mathematical models predict species richness and map region-wide diversity patterns. Next, they estimated cell-wide inventory completeness related to human geographical factors.

They found that the observed patterns of hawkmoth species richness are strongly determined by the number of available records in grid cells. Vegetation type is an important factor in estimated total richness, together with heat, energy availability and topography. Their model identified three centres of diversity: Cameroon coastal mountains, and the northern and southern East African mountain areas. Species richness is still under-recorded in the western Congo Basin and in southern Tanzania/Mozambique.

What does this mean? It means that sampling (and therefore our knowledge) of biodiversity is heavily biased. We have good data and information where there is higher population density; for more accessible and less remote areas; for protected areas and for certain areas where there was collecting in colonial periods. If it is easy to get to, not too difficult to access, there are more people around and there have been longer histories of collecting: we have better knowledge.

This is important in how we understand biodiversity and in how we make decisions with our knowledge to protect forests or other areas. But this study means that we can take account of data gaps if we are looking at larger scale patterns of diversity. It shows that baselines for broad diversity patterns can be developed using models and what data there is available. We can identify the "known unknowns" in terms of information gaps in part by looking at human geographical features - the models can help set priorities for future exploration and collection as well as informing our understanding of biodiveristy.